Data Scientist II

Public Storage • Full-time • Burbank, California, United States • 1d ago

Company Description

Since opening our first self-storage facility in 1972, Public Storage has grown to become the largest owner and operator of self-storage facilities in the world. With thousands of locations across the U.S. and Europe, and more than 170 million net rentable square feet of real estate, we're also one of the largest landlords.

We've been recognized as A Great Place to Work by the Great Place to Work Institute. And, our employees have also voted us as having Best Career Growth, ranked us in the Top 5% for Work Culture, and in the Top 10% for Diversity and Inclusion.

We're a member of the S&P 500 and FT Global 500. Our common and preferred stocks trade on the New York Stock Exchange.

Public Storage is the nation’s leading self-storage provider, recognized for its iconic orange doors and commitment to delivering simple, reliable solutions to millions of customers across the country. We are expanding our creative team to enhance our consistent and engaging visual brand presence.

Job Description

At Public Storage, data science sits right next to the decisions. Prices move every day across thousands of locations, and our models help decide them. As a Data Scientist II, you’ll be the go-to data scientist for one part of the business: customer and revenue, capital allocation (acquisitions, development and finance), or operations. You’ll be part of their planning, shape the questions, and build the models they rely on.

We’re a small, senior team that works like a startup inside an S&P 500 company. Ideas get to production quickly, you own your work, and it shows up in the business rather than in a slide deck.

What you’ll do

Build and own production models. Pricing and elasticity, promotions, marketing effectiveness, demand forecasting, retention, lifetime value and recommendation. You’ll write production-grade Python and SQL, and use LLMs, embeddings and RAG where they’re the right tool.
Be the data science partner for your business area. Join their planning, turn ambiguous questions into well-scoped problems, and present results as decisions and dollars, not model metrics.
Run experiments that change what we do. Design and analyze A/B tests, holdouts and quasi-experiments. Set the success metric before launch and report the honest result after it.
Build once, reuse everywhere. Work from our shared customer and real estate data and certified metrics, and add reusable models to a shared catalog so the next problem starts faster.
Grow the team around you. Mentor analysts and junior scientists through code review and pairing, and back up a teammate’s business area when needed.
Own the full model lifecycle. Versioning, retraining, drift monitoring and documentation, meeting our model governance standards before anything goes live.

What success looks like

Business partners depend on your models every week.
Your experiments change what the company does, not just what it knows.
Junior scientists get better because of your code reviews.

Qualifications

Bachelor’s degree in a STEM or quantitative field, such as statistics, computer science, economics, engineering or applied mathematics.
3+ years in a production data science role, or 2+ years with a Master’s, or 1+ year with a PhD.
Strong SQL and Python in production settings, not notebooks alone.
Applied statistics in both frequentist and Bayesian frameworks, plus experimental design and causal inference.
A track record of shipping models that drove measurable business outcomes.
Experience with a cloud data platform. Google Cloud and BigQuery preferred; Snowflake, AWS or Azure considered.
Able to turn an ambiguous business question into a scoped problem, and explain uncertainty and trade-offs to non-technical partners.

Preferred

5+ years of production data science in pricing, revenue management, marketing, real estate, retail or consumer analytics.
The modern ML stack: scikit-learn, XGBoost/LightGBM, PyTorch, PyMC, Prophet, and experiment tracking with MLflow or Vertex AI.
MLOps experience: model registries, CI/CD, scheduled retraining and monitoring.
dbt and orchestration tools such as Airflow or Cloud Composer.
Hands-on experience with LLM applications, vector search, RAG or agentic AI workflows.

Additional Information

Workplace

One of our values pillars is to work as One Team and we believe that there is no replacement for in-person collaboration but understand the value of some flexibility. Public Storage teammates are expected to work in the office five days each week with the option to take up to three flexible remote days per month.

Public Storage is an equal opportunity employer and embraces diversity. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other protected status. All qualified candidates are encouraged to apply.

**Sponsorship for Work Authorization is not available for this posting. Candidates must be authorized to work in the U.S. without restrictions or requiring sponsorship now or in the future. We do not provide training plans or support for F-1 OPT, STEM OPT extensions, or future visa sponsorship.**